SCREENING OF AMINE FOR CO2 CAPTURE

USING COSMO-RS MODEL

AMIR AIMAN BIN ABD RAZAK

CHEMICAL ENGINEERING

UNIVERSITI TEKNOLOGI PETRONAS

JANUARY 2015

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Screening of Amine for CO2 Capture Using COSMO-RS Model

by

Amir Aiman Bin Abd Razak

14766

Dissertation submitted in partial fulfilment of

the requirements for the

Bachelor of Engineering (Hons)

(Chemical Engineering)

JANUARY 2015

Universiti Teknologi PETRONAS

32610 Bandar Seri Iskandar

Perak Darul Ridzuan

i

CERTIFICATION OF APPROVAL

Screening of Amine for CO2 Capture Using COSMO-RS Model

by

Amir Aiman Bin Abd Razak

14766

A project dissertation submitted to the

Chemical Engineering Programme

Universiti Teknologi PETRONAS

in partial fulfilment of the requirement for the

BACHELOR OF ENGINEERING (Hons)

(CHEMICAL ENGINEERING)

Approved by,

_____________________

(AP. Dr. Mohamad Azmi B Bustam @ Khalil)

UNIVERSITI TEKNOLOGI PETRONAS

BANDAR SERI ISKANDAR, PERAK

January 2015

ii

CERTIFICATION OF ORIGINALITY

This is to certify that I am responsible for the work submitted in this project, that the

original work is my own except as specified in the references and acknowledgements,

and that the original work contained herein have not been undertaken or done by

unspecified sources or persons.

____________________________

AMIR AIMAN BIN ABD RAZAK

iii

ABSTRACT

The significant and rapid reduction of greenhouse gas emissions is recognized as

necessary to mitigate the potential climate effects from global warming. The

postcombustion capture (PCC) and storage of carbon dioxide (CO2) that produced

from the use of fossil fuels for electricity generation and from contaminant

presented in natural gas are a key technologies needed to achieve these reductions.

The most mature technology for CO2 capture is reversible chemical absorption into

an aqueous amine solution. Although, amine-based solvents became promising

solvents in CO2 absorption process, the selection of appropriate amine for specific

process is impossible without a prior screening. This work presents the screening

technique to identify the potential amine for CO2 capture using COSMO-RS

model. To achieve this target we investigated 57 tertiary amine based CO2

absorbents with different chemical structures. Screening procedures were carried

out based on their CO2 absorption rate and loading amount. The screening starts

with the optimization of the amine compound geometry using TURBOMOLE.

Then, we proceed with the prediction of the basicity of every amine candidates

using COSMO-RS. The basicity were then compared with experimental results to

check the reliability of the prediction. The correlation between predicted basicity

value and amine performance in absorbing CO2 was established. Several high

performance amine absorbents for CO2 capture were recommended for future

studies.

iv

TABLE OF CONTENTS

CERTIFICATION OF APPROVAL i

CERTIFICATION OF ORIGINALITY ii

ABSTRACT iii

ACKNOWLEDGEMENT vii

CHAPTER 1 1

1. INTRODUCTION 1

1.1. Background of Study 1

1.2. Problem Statement 5

1.3. Objective 5

1.4. Scope of Study 5

CHAPTER 2 6

2. LITERATURE REVIEW 6

2.1. CO2 Capture Technology 6

2.2. Amine 8

2.3. COSMO-RS as Tools to Predict the Basicity of Amine 14

CHAPTER 3 16

3. METHODOLOGY 16

3.1. Identification of amine candidates 19

3.2. Optimization of amine geometry using TmoleX 24

3.3. Prediction of basicity of amine in water using COSMOthermX 26

3.4. Gantt Chart and Key Milestone (FYP II January Semester) 29

CHAPTER 4 30

4. RESULT AND DISSUSION 30

4.1. Correlation between experimental and calculated pKa 30

4.2. Correlation between predicted basicity value with absorption rate and

absorption amount. 35

CHAPTER 5 40

5. CONCLUSION & RECOMENDATION 40

REFERENCES 41

APPENDICES 42

v

LIST OF FIGURES

Figure 1: Malaysia Primary Energy Supply (Suruhanjaya Tenaga). 2

Figure 2: The chemical absorption and desorption of CO2 in postcombustion 7

Figure 3: Basic types of amines 9

Figure 4: Structure of polyamine 9

Figure 5: Summary of the methodology. 18

Figure 6: 2-(diisopropylamino)ethanol molecular structure drawn inside TmoleX 24

Figure 7: Geometry optimization was set on TZVP atomic basis 25

Figure 8: Molecular orbitals for the molecule was generated 25

Figure 9: Calculation were performed on the density functional theory (DFT) at the

B3-LYP/TZVP level with resolution of identity (RI) 26

Figure 10: Setting of convergence parameter for the geometry optimization 26

Figure 11: The optimized imidazole molecule and ion was imported into COSMO 27

Figure 12: Prediction of basicity using COSMO-RS 27

Figure 13: Prediction of pKa value of amine in water at temperature of 25oC 28

Figure 14: Graph of pKa values calculated using COSMO-RS//BP/TZVP versus

Experimental value 32

Figure 15: Distribution of pKa value for different amine types 33

Figure 16: Predicted pKa value vs Absorption Rate 37

Figure 17: Predicted pKa value vs Absorption Amount 37

vi

LIST OF TABLES

Table 1: Gantt Chart for the FYPII 29

Table 2: Experimental and Predicted pKa data using COSMO-RS for 46 amines

molecule 30

Table 3: Predicted pKa value from this work is to be compare with the absorption

rate and absorption amount 35

Table4: Prediction of absorption rate and absorption capacity 38

vii

ACKNOWLEDGEMENT

Firstly, I would like to express my utmost gratitude and thankfulness to Allah

SWT Lord Almighty whom had made my final year to be very meaningful. The

preparation of this final year project “Screening of Amine for CO2 Capture Using

COSMO-RS Model” would not be possible without the guidance of my supervisor,

AP. Dr. Mohamad Azmi B Bustam @ Khalil and Dr. Dr Girma Gonfa who have help

me a lot throughout this FYP project . He gave me continuous guidance throughout

the period. Gave important advices and comment on the work especially in writing

the report.

I owe my profound gratitude to Chemical Engineering Department of ies

Universiti Teknologi PETRONAS (UTP), Labs and Facilities Technical Assistants,

Postgraduates of Chemical Engineering Final Year course mates. Thank you to the

coordinator, Dr Asna for her effort conducting few adjunct lectures to guide the

student in completing the final year project.

At the end of the note, thank you everyone and may Allah SWT bless us all.

1

CHAPTER 1

INTRODUCTION

1. INTRODUCTION

1.1. Background of Study

Scientific findings have shown that, if global warming keep on rising until it reach

2°C or more above the pre-industrial temperature, the risk of irreversible and

catastrophic environmental change such polar ice melting will be unstoppable [1]. This

global warming effect is attributed to increasing concentrations of CO2 and other

greenhouse gases in the earth’s atmosphere. To overcome this problem, there are a

number of initiatives to reduce CO2 emissions around the world currently being carried

out. Worldwide, there are more than 8,000 large stationary CO2 sources whose

cumulative emissions in 2005 were reported as being 13,466 MtCO2/year [2]. This

problem become even worst as the population keeps growing with increasing demands

for more energy intensive lifestyles. Based on the study, it is well accepted that fossil

fuels will continue to become the most important source of both heat and power

generation and also in heavy industrial manufacturing operations for years to come

[3]. According to the United Nation-Intergovernmental Panel on Climate Change

(IPCC) and the IPCC projects global warming will keep increasing between 1.8 to 4°C

in this century. This can be avoided if international community acts to cut down

greenhouse gas (GHG) emissions.

Through the Kyoto Protocol (1997), developed countries agreed to reduce their

CO2 emissions by 5.2% below their 1990 levels. European Union (EU) has even

agreed in 2008 to reduce GHG emissions to 20% below 1990 levels by 2020. Malaysia,

as a party of the UNFCCC and has ratified the Kyoto Protocol, has already committed

2

to formulate, and implement programs to mitigate climate changes [17]. However,

given the increasing fossil energy consumption, the CO2 emission level is likely to

continue increasing, so even greater reductions in the CO2 emissions will be required

in the future. It was calculated that, for example, emissions of CO2 may need to be

reduced by more than 60% by 2100, in order to stabilize the atmospheric concentration

of CO2 at no more than 50% above its current level [4].

The largest contributor to emission of CO2 is the use of fossil fuel which produce

around 21.3 billion tonnes of CO2 per year [36]. Apart from being generated as a

product of fuel combustion, CO2 also come in the form of contaminant gas inside

natural gas. Due to relatively low emission, natural gas was currently one of the most

attractive and fastest growing fuel of world primary energy consumption. The major

contaminates present in natural gas feeds is CO2 which must be removed as it reduces

the energy content of the gas, affect the selling price of the natural gas and it becomes

acidic and corrosive in the presence of water which has a potential to damage the

pipeline and the equipment system.

Figure 1: Malaysia Primary Energy Supply (Suruhanjaya Tenaga).

34%

2.30%19%

45.10%

Malaysia Primary Energy Supply

Oil and Products Hydropower Coal and Coke Natural Gas

3

Figure 1 showing natural gas’ share of Malaysia’s primary energy mix in 2011.

Since natural gas was introduced to the Malaysia power sector, it continuously become

the most preferred fuel for power generation.

Carbon capture and storage (CCS) technologies are a promising route to achieve a

meaningful reduction in CO2 emissions in the near-term. CCS is defined as a system

of technologies that integrates CO2 capture, transportation and geological storage.

Emission reduction targets such as 80- 90% of CO2 emissions from fixed point sources

are routinely discussed in the context of targets achievable by CCS technologies. Each

stage of CCS is in principle technically available and has been used commercially for

many years (IEA 2008). However, various competing technologies, with different

degrees of maturity, are competing to be the low-cost solution for each stage within

the CCS value chain. It is vital to select methods of CO2 capture that are optimal not

only in terms of their capital and operating cost, but also in terms of their

environmental impact. There are numerous different technologies are being used by

industry to remove CO2 from gas streams, where it was an undesirable contaminant

needed to be separated as a product gas. There are three technology options that are

generally accepted as being suitable for commercial deployment in the near to medium

term; post-combustion CO2 capture using amine solvents, oxy-fuel combustion and

calcium looping technologies. Post-combustion amine-based CO2 capture is seen to be

the most promising alternative because it is an “end of pipe” technology which means

it can be installed either to the existing plant or to a new plant without affecting the

current plant configuration.

However, due to unlimited possible candidates of amine-based solvent, the

evaluation of amine for CO2 capture is time consuming and expensive if carried out

experimentally. The opportunity to select new amine may also be missed. Hence, to

effectively select the best amine with desired properties, all possible amines must be

preliminary screened in a systematic way. A predictive method for quantitative

evaluation which is applicable to a range of solutes or solvents is desired to avoid the

screening of a large amount of candidates. For this purpose, a quantum chemical

approach combined with a solvation model is promising because it is applicable to a

variety of chemical species that need to be investigated with the same parameters by

4

just changing the molecular structure [19]. In this method, molecular geometries are

optimized and the free energy of each species in solution is calculated. On the basis of

these calculated free energies, we can estimate the species distribution at equilibrium

in the solution [20]. The conductor-like screening model for real solvents (COSMO-

RS) has been developed as a general and fast method for the a prior prediction of

thermodynamic data for liquids such as activity coefficients, pKa values, partition

coefficients, vapor pressures, and solubility [21]. COSMO-RS is based on cheap

quantum chemical calculations followed by statistical thermodynamics to give the free

energies of all species in solution. The COSMO-RS method, among others, employs a

density functional theory (DFT) at the BP/TZVP level and has been shown to be fast

and accurate in various systems [22]. An important advantage of the COSMO-RS

model is that it predicts the properties of component in a mixture without using any

experimental data. In this work potential amines were screened to estimate its pKa

value using COSMO-RS and the relationship between the estimated pKa value and the

performance of amine which in this case is the absorption rate and absorption amount

were established. It is found that, generally the performance of amine is directly

proportional to its basicity.

5

1.2. Problem Statement

Carbon capture and storage is one of the promising ways to reduce CO2 emission

to the air either from burning of fossil fuel or from the contaminant inside natural gas.

Currently, the most matured technology in this field is postcombustion amine-based

solvent. However, there are unlimited types of amine-based solvent being discovered

worldwide but to evaluate the capabilities for every single amine for CO2 capture is

time consuming and expensive if carried out experimentally. The opportunity to select

new amine may also be missed. Hence, to effectively select the best amine with desired

properties, all possible amines must be preliminary screened in a systematic way.

1.3. Objective

The objectives of this work are as follow:

a) To optimize the structure of amine and generate COSMO file Using

Turbomole to predict the pKa values of amine candidates using

COSMOthermX.

b) To validate the predicted pKa values with the experimental literature data.

c) To establish the relationship between basicity of amine with it absorption

rate and absorption amount.

d) To suggest promising amine candidate based on this findings.

1.4. Scope of Study

The scopes of study for this particular project are:

a) This work focused on the screening of amine that suitable for CO2 capture.

b) The scope of this work was limited to only tertiary amine due to limitation

of time.

c) The tool used to estimate the thermodynamic properties of the amine is

COSMO-RS.

d) The tool used to optimize the structure of the amine geometry is

TURBOMOLE.

6

CHAPTER 2

LITERATURE REVIEW

2. LITERATURE REVIEW

2.1. CO2 Capture Technology

Relating to the environmental concerns that have been addressed in the previous

section, there is a need to develop technologies to reduce CO2 emission. As stated

before, there are three technologies option that generally acceptable which are post-

combustion amine-based CO2 capture, oxy-fuel combustion process and calcium

looping technologies.

2.1.1. Amine based CO2 capture process description

In amine-based CO2 capture in figure 2, the gas stream with high CO2 content is

contacted with the “lean” amine-based solvent stream which flows from top of

absorption column through a packing materials and during the contact, the amine-

based solvent will absorb the CO2 molecule from the CO2 rich gas stream [1]. This

amine solvent reacts with CO2 and once the CO2 has reacted with the aqueous amine

solution, it forms a carbonate salt [1]. When the “rich” solvent stream reaches the

bottom of the column, it is directed to a solvent regeneration process, which is another

gas-liquid contacting column with a condenser at the top and a reboiler at the bottom.

The purpose of the reboiler is to heat the incoming liquid stream to a suitable

temperature in order to both break the chemical bonds formed and to provide a vapour

stream to act as a stripping fluid. The purpose of the overhead condenser is both to

provide a reflux liquid stream to the column and to ensure that the top-product stream

is as pure as possible. The regenerated amine can be recycled whereas the CO2 is

compressed and transported away as a liquid. Given the reactive nature of the

7

absorption, amine based solvent processes are well-suited to capturing CO2 from

dilute, low pressure streams. This makes this technology applicable to the majority of

existing large, fixed-point sources of CO2.

There are always pros and cons in using any technologies. In case of amine-based

CO2 capture, the advantage is, it is an “end of pipe” technology which means it can be

installed either to the existing plant or to a new plant without affecting the current plant

process [5]. However, this technology have distinct disadvantage over their cost either

capital cost or operational cost. It is expected that by using this technology, it will

reduce the thermal efficiency of a modern power plant from approximately 45% to

approximately 35% [6]. This is due to the cost for compressing the CO2 before it can

be transported, costs related to transportation of flue gas and cost for solvent

regeneration [1]. In addition, these processes are expected to consume between 0.35

and 2.0 kg of solvent per tons of CO2 captured which will directly increase the cost to

replace the lost solvent [7].

Figure 2: The chemical absorption and desorption of CO2 in postcombustion process

8

2.2. Amine

Chemical absorption technology is a matured technology which widely used

on a large scale across several industries [8]. Therefore, it is assumed that no major

innovation and improvement will occur in the design of both the column internals

and the processor. Any new innovation or major scope for reducing the costs

associated with these processes lies in the selection and design of new, advanced

sorbent materials as it is the solvent which determines the thermodynamic and

kinetic limits of the process [1]. The solvent chemistry also will determine the type

and seriousness of any environmental and public health impacts because of the

emissions of organic solvents, or their associated degradation or corrosion

products. Therefore, the selection of appropriate solvents is not as simple. In terms

of solvent selection, amines have traditionally been the solvents of choice, with a

primary alkanolamine, monoethanolamine (MEA) typically considered to be the

benchmark solvent with which alternative solvents must be compared. Other

compounds which are often considered are sterically hindered compounds such as

2-amino-2-methyl-1-propanol (AMP), secondary amines such as diethanolamine

(DEA) and tertiary amines such as methyldiethanolamine (MDEA) [1].

2.2.1. Types of Amines

Amines are organic compounds and functional groups that contain a

basic nitrogen atom with a lone pair. Amines are derivatives of ammonia,

where in one or more hydrogen atoms have been replaced by a substituent such

as an alkyl or aryl group. Amines fall into different classes depending on how

many of the hydrogen atoms are replaced [23].

The first class of amine is primary amine. In primary amines, only one

of the hydrogen atoms in the ammonia molecule has been replaced which

means that the formula of the primary amine will be RNH2 where "R" is an

alkyl group. Secondary amine is amines with two of the hydrogen in an

ammonia molecule have been replaced by hydrocarbon groups and tertiary

amine is when all of the hydrogens in an ammonia molecule have been replaced

by hydrocarbon groups [24]

9

Primary Amine Secondary Amine Tertiary Amine

Figure 3: Basic types of amines

Amine can also be further classified into either sterically free or

sterically hindered amine. Sterically hindered amine is amines for which either

a primary amino group is attached to a tertiary carbon atom or a secondary

amino group is attached to a secondary or tertiary carbon atom [24]. Polyamine,

as in figure 4, in the other hand is another type of amine which is an organic

compound having two or more primary amino groups (NH).

Figure 4: Structure of polyamine

10

2.2.2. Amine Reaction with CO2

The reactive nature of the aqueous solutions of amines with CO2 system

is well known, and there is a large body of experimental and theoretical work

in place detailing the mechanism and rates of these reactions. In addition to the

ionic speciation equilibria owing to the disassociation of CO2 and the amines

in aqueous solution, the principal reaction of interest between CO2 and a

primary and secondary amine (in aqueous media) is the formation of a

carbamate, which is typically considered to occur via the formation of a

zwitterion, and subsequent base catalysed deprotonation of the zwitterion [4].

Generally, primary and secondary amines (represented as R1R2NH) can react

with dissolved CO2 to form a carbamic acid (R1R2NCOOH). Depending upon

its acidity, it may then give up a proton to a second amine molecule forming a

carbamate (R1R2NCOO-) according to an overall stoichiometry of 2 as shown

below [18].

𝐶𝑂2 + 𝑅1𝑅2𝑁𝐻 ⇌ 𝑅1𝑅2𝑁𝐶𝑂𝑂𝐻

𝑅1𝑅2𝑁𝐶𝑂𝑂− + 𝐻+ ⇌ 𝑅1𝑅2𝑁𝐶𝑂𝑂𝐻

𝑅1𝑅2𝑁𝐻 + 𝐻+ ⇌ 𝑅1𝑅2𝑁𝐻2+

Via this pathway two moles of amine are consumed per mole of CO2 if the

carbamic acid is acidic, which is generally assumed to be the case. Kinetically

and thermodynamically this reaction pathway is generally favored for primary

and secondary amines [25].

A second reaction pathway that also contributes to CO2 absorption is CO2

hydration to form bicarbonate. In this pathway an amine molecule (represented

as R1R2R3N) simply acts as a proton accepting base, and possibly a catalyst,

for the hydration of CO2 [26].

𝐶𝑂2 + 𝐻2𝑂 ⇌ 𝐻𝐶𝑂3− + 𝐻+

𝑅1𝑅2𝑅3𝑁 + 𝐻+ ⇌ 𝑅1𝑅2𝑅3𝑁𝐻+

Via this pathway one mole of amine is consumed per mole of CO2, so in terms

of capacity it is more efficient. For tertiary and some sterically hindered

11

primary and secondary amines this is the only pathway contributing to

absorption. However, this pathway is generally less favorable kinetically than

carbamate formation [25].

2.2.3. Parameters that affect performance of amine

In designing the absorption system, some parameters need to be

properly selected to optimize the absorption process in terms of a cost-benefits

analysis [11]. First parameter is the partial pressure of the CO2 in the absorber.

A high partial pressure of CO2 in the absorber is beneficial to the absorption

process, but raising the pressure of flue gases at the plant exit requires power

for the blower. Thus, these two factors need to be properly determine in the

selection of the absorption pressure. Since the flue gas has a large concentration

of N2, while CO2 concentration is only approximately 10 %, any increase of

the total pressure only raise the CO2 pressure by 0.1. Therefore the energy for

flue gas compression should be the minimum possible and the pressure in the

absorber should be the lowest possible, by using a low-pressure drop tray

design in the packed column [4].

Secondly, another important factor in the design of both the absorber and

the stripper is the solvent flow rate. The higher the flow rate, the lower the

number of trays in the absorber and the stripper. The higher the flow rate, the

higher is the solvent cost and the greater the diameter of the absorber and the

stripper. Therefore, the optimum flow rate can be determined by the balance of

these two competing factors: cost of solvent and number of trays in the absorber

and stripper [4].

Next, the selection of the amine type is also a very important factor for the

performance and cost of the capture system. Monoethanolamine (MEA) is the

more reactive amine, and a 30 % amine solution allows the number of trays in

the columns, and so the solvent flow rate, to be minimized, thus reducing the

overall costs. Using secondary and tertiary amines is generally more expensive

in terms of capital and operational costs compared to MEA. It must be noted

12

that the energy required for regeneration is proportional to the sum of the heat

of reaction and the latent heat of vaporization of the solvent [13]. By looking

at the different solvents that can be used in CO2 capture systems, it is possible

to see that tertiary amines require less energy for regeneration. However, they

have a very low absorption rate and thus a lower mass transfer rate and,

ultimately, a larger equipment size for absorption and stripping will be

required. For this reason, blending secondary and tertiary amines with primary

ones or adding activators to MEA can reduce the heat of regeneration without

significantly reducing the reaction rate. In order to increase CO2 loading in the

amine aqueous solution and reduce the regeneration heat, sterically hindered

amines, such as those used by Mitsubishi Heavy Industries in their CO2

recovery plants, have been tested and studied (Mimura, 1995, 1997). They have

some very interesting properties capable of improving the performance of the

system shows that sterically hindered amines such as KS-1 have a higher CO2

absorption capacity than MEA (Mimura, 1995). This is mainly due to the fact

that the chemical reactions do not produce the carbamate ion; this allows the

CO2 loading to be increased because 1 mol of CO2 can react with 1 mol of

amine rather than 0.5 mol.

13

2.2.4. Basicity of Amine

The dissociation constant (pKa) is one of the key parameter that affects the

performance of amine. It is an important factor in the selection of an amine

based absorbent for acid gas removal and also serves as a first indicator of the

reactivity of various amine-based absorbents towards CO2 [4]. Dissociation

constants provide the basic strength of the amine-based absorbent at a specific

temperature. Their temperature dependency and reaction enthalpy will be

reflected in the overall reaction enthalpy of CO2 with the amine based solvent

[4]. When the basic strength is reduced, the tendency to remove a proton from

the intermediate zwitterions formed during the reaction with CO2 will be lower.

Based on study by Puxty & Rowland [18], primary and secondary amines

in general do not appear to show a strong correlation with pKa. This is not

surprising as carbamate formation has lower sensitivity to pH, and thus amine

pKa, is dependent on the carbamate stability constant which varies from amine

to amine. The tertiary amines do show a strong dependence on pKa consistent

with bicarbonate formation being the dominant reaction pathway for CO2

absorption. This is because the CO2 hydration reaction is independent of the

amine but is strongly pH dependent due to the small stability constant for

bicarbonate formation. The mixed amines all contain primary or secondary

functionality and also show little correlation with pKa.

Based on this correlation, screening of amine as an absorption material for

CO2 capture can be done on the basis of its pKa value.

14

2.3. COSMO-RS as Tools to Predict the Basicity of Amine

2.3.1. COSMO-RS

Conductor-like Screening Model for Real Solvents (COSMO-RS) is

quantum chemistry based statistical thermodynamics model for the prediction

of thermodynamic properties of fluids and liquid mixtures. COSMO-RS

predicts thermodynamic properties of liquid mixtures, such as, activity

coefficient, vapor pressures, and solubility by using the molecular structure of

solute and solvent as initial inputs.

2.3.2. Modeling

Yamada, Shimizu, Okabe, Matsuzaki, Chowdhury & Fujioka in 2010 [20]

and Scientific Computing & Modeling website [28] have come out with a

model to predict amine basicity. In an aqueous solution, amine B reacts with

H+ as a base and the species distribution in the equilibrium is related to its

basicity.

𝐵𝐻+ + 𝐻2𝑂 ⇋ 𝐵 + 𝐻3𝑂+ ⋯ (1)

𝑝𝐾𝑎 − 𝑙𝑜𝑔10 ([𝐵][𝐻3𝑂+]

[𝐵𝐻+]) ⋯ (2)

In this model, molar concentration is used in units of moles per litre instead of

activity and the concentration of H2O is assumed to be constant. The Gibbs

free energy of reaction (1) is the difference between the total free energies of

the reactants and products.

𝛥𝐺𝑅1 = 𝐺(𝐵) + 𝐺(𝐻3𝑂+) − 𝐺(𝐵𝐻+) − 𝐺(𝐻2𝑂) ⋯ (3)

From the relation between the reaction free energy and the equilibrium

constant, ΔGR = -RT ln K, where R is the gas constant, the following equation

is given at T = 298.15 K using energy units of kilocalories per mole.

𝑝𝐾𝑎 = 0.733𝛥𝐺𝑅1 − 1.74 ⋯ (4)

In aqueous amine solutions, CO2 is absorbed by the formation of carbamate or

bicarbonate anions

15

2𝐵 + 𝐶𝑂2 ↔ 𝐵′𝐶𝑂𝑂− + 𝐵𝐻+ ⋯ (5)

𝐵 + 𝐶𝑂2 + 𝐻2𝑂 ↔ 𝐻𝐶𝑂3− + 𝐵𝐻+ ⋯ (6)

where B′ implies the deprotonation of the neutral amino group. It should be

noted that the species in the above relations are also involved in other reactions

in the system. However, we assume that the equilibrium constants of reactions

(5) and (6) are independent of other reactions. Hence, a ratio between

carbamate and bicarbonate anions at equilibrium is represented by the

equilibrium constants as follows.

𝑟 =[𝐵′𝐶𝑂𝑂−]

[𝐻𝐶𝑂3−]

=[𝐵]𝐾𝑅5

[𝐻2𝑂]𝐾𝑅6=

[𝐵]

[𝐻2𝑂]× exp (

−(∆𝐺𝑅5 − ∆𝐺𝑅6)

𝑅𝑇) ⋯ (7)

The experimental data in this work were obtained under conditions that

simplify the treatment of the above equation. Under these conditions, we ignore

the presence of 𝐶𝑂32−, 𝐻3𝑂+ and 𝑂𝐻− ions in the charge balance equation.

[𝐵′𝐶𝑂𝑂−] + [𝐻𝐶𝑂3−] = [𝐵𝐻+] ⋯ (8)

Using eqs (2) and (8), eq (7) may be adjusted to evaluate the calculation results

of ΔGR5 and ΔGR6 using measurable parameters

log10 ([𝐵′𝐶𝑂𝑂−]

[𝐻𝐶𝑂3−]

)

= 𝑝𝐻 − 𝑝𝐾𝑎

+ log10 {[𝐵′𝐶𝑂𝑂−] + [𝐻𝐶𝑂3

−]

[𝐻2𝑂]exp (

−(∆𝐺𝑅5 − ∆𝐺𝑅6)

𝑅𝑇) } ⋯ (9)

Where pH is defined as − log10[𝐻3𝑂+].

16

CHAPTER 3

METHODOLOGY

3. METHODOLOGY

In the COSMO-RS method, the free energy of each species in solution is

obtained by calculating its chemical potential with a statistical thermodynamics

algorithm in which a measure of the system affinity to molecular surface polarity

is calculated iteratively. COSMO-RS method to predict the thermodynamic

properties of fluids and liquid mixtures is a unique way for priori prediction of

behavior of pure fluids in their mixtures on the basis of unimolecular quantum

chemical calculations [30]. Original work from Klamt et al. has already described

the COSMO-RS theory comprehensively [31]. One main advantage of COSMO-

RS model is that it is capable to predict the thermodynamic properties of any

component in a mixture without using any experimental information. It uses the

molecular structure of the solute/component as single initial input. Thus, it can be

used to predict the basicity of amine solution in water.

The reliability of COSMO-RS to predict the pKa value of organic bases has been

shown by Eckert and Klamt in their work [31]. Therefore, in this work, COSMO-

RS was used to predict the basicity of amine solution in water and finally develop

the relationship between amine basicity with its performance in CO2 capture.

Based on literature [20], standard procedure for COSMO-RS calculations of

the pKa values consist of two steps. In the first step, continuum solvation COSMO

calculations of electronic density and molecular geometry optimizations were

carried out at the B3-LYP/TZVP level using the resolution of identity (RI)

17

approximation [29]. The structures are fully optimized and the quantum chemical

calculations performed for each molecule with TURBOMOLE program package.

Then, the optimized geometries were used for a single-point COSMO

calculation at the BP/TZVP level with the RI approximation. COSMO-RS

calculation was performed using COSMOthermX program.

Predicted basicity values generated were then compared and plotted against the

experimental results to confirm for the reliability of the prediction. Finally, the

relationship between predicted basicity versus the absorption rate and absorption

amount was established. Based on that relationship, we have suggested few amine

candidates that potentially good candidates for CO2 capture.

18

Geometry of amine compounds was optimized at the B3-LYP/TZVP level using the resolution of identity (RI) and cosmo file was generated using

Turbomole.

Basicity of amine compounds in water was predicted using COSMOthermX

Predicted basicity value from COSMO was validated by comparing the value with the experimental value reported in literature

Relation between basicity of amine with its absorption capacity and absorption rate was established

Figure 5: Summary of the methodology.

19

3.1. Identification of amine candidates

We selected 57 tertiary amine based absorbents with broad range of structures. They

are illustrated as below.

Alkanolamine

N

HO2-(dimethylamino)ethanol

N

OH

2-(diisopropylamino)ethanol

N

OH

HOmethyldiethanolamine

N

HO OH

HOtriethanolamine

1-diethylamino-2-propanol

OH

N

2-(dimethylamino)-2-methyl-1-propanol

OH

N

3-(dimethylamino)-1,2-propanediol

HO

OH

N

3-diethylamino-1,2-propanediol

HO

OH

N

3-diethylamino-1-propanol

OHN

4-diethylamino-2-butanol

HO

N

4-ethyl-methyl-amino-2-butanol

OHN

N-isopropyldiethanolamine

N

OH

HO

2-diethylaminoethanol

N

HO

1-dimethylamino-2-propanol

OH

N

20

N-ethyldiethanolamine

N

HO

OH

3-dimethylamino-1-propanol

HO N

4-(Dimethylamino)-1-butanol

HO

N

6-dimethylamino-1-hexanol

HO

N

N-methyldiethanolamine

N

OH

HO

N-tert-butyldiethanolamine

N

HO

HO

3-dimethylamino-2,2-dimethyl-1-propanol

HO

N

N- methyl -N- (N, N- dimethylaminoethyl ) ethanolamine

N

HO N

N- methyl -N- (N, N- dimethylaminopropyl) ethanolamine

N

OH

N

N- (N, N- dimethylaminoethyl) diethanolamine

N

HO

OH

N

2-[2-(Dimethylamino)ethoxy]ethanol

ON

OH

2-(2-Diethylaminoethoxy)ethanol

O

NHO

21

Alkylamine

Triethylamine

N

Trimethylamine

N

1-Dipropylaminopropane

N

N,N,N',N'-Tetramethylethylenediamine

N

N

Diamine

N,N,N',N'-tetraethylethylenediamine

N

N

N,N,N',N'-Tetraethylmethanediamine

N

N

N,N,N',N'-Tetramethylpropylenediamine

N

N

22

Cyclic Amine

4-ethylmorpholine

N O

2-morpholinoethanol

ON

HO

1,4-bis(2-hydroxyethyl)piperazine

N N

OH

HO

3-morpholino-1,2-propanediol

HO

OH

ON

1-methyl-2-piperidineethanol

N

OH

3-hydroxy-1-methylpiperidine

N

HO

1-(2-hydroxyethyl)pyrrolidine

N

HO

3-pyrrolidino-1,2-propanediol

OH

HO

N

1-(2-hydroxyethyl)piperidine

N

HO

1-ethyl-3-hydroxypiperidine

N

HO

3-piperidino-1,2-propanediol

HO

OH

N

1-n-Butylpiperidine

N

1,2-Dimethylpiperidine

N

1,2-Dimethylpyrrolidine

N

1-Ethyl-2-methylpiperidine

N

1-Ethyl-2-methylpyrrolidine

N

1-Methyl-2-n-butyl-pyrrolidine

N

23

N-Ethylpiperidine

N

1-n-Propylpiperidine

N

N-Methyltrimethyleneimine

N

N-Methylpyrrolidine

N

N-Methylpiperidine

N

Aromatic Amine

imidazole

N

HN

4-methylimidazole

N

NH

1,2-dimethylimidazole

N

N

24

3.2. Optimization of amine geometry using TmoleX

In order to optimize the structure inside the Turbomole, first, we have to draw the

structure for each amine candidates. Then, the drawing will be transfer into the 3D

Molecular Builder inside the TmoleX software. Below is the example of the 2-

(diisopropylamino)ethanol structure that have been redraw inside the TmoleX.

N

OH

2-(diisopropylamino)ethanol

Figure 6: 2-(diisopropylamino)ethanol molecular structure drawn inside TmoleX

25

Figure 7: Geometry optimization was set on TZVP atomic basis

Figure 8: Molecular orbitals for the molecule was generated

26

Figure 9: Calculation were performed on the density functional theory (DFT) at the

B3-LYP/TZVP level with resolution of identity (RI)

Figure 10: Setting of convergence parameter for the geometry optimization

Steps above was repeated for all of the amine candidates and its respective ions.

3.3. Prediction of basicity of amine compound in water using COSMOthermX

After the geometry optimization process for all of the amine was finished, the data was

then imported into COSMOthermX to predict the basicity. COSMO-RS are capable to

predict the thermodynamic properties of fluids and liquid mixtures by using the

molecular structure of the solutes and solvents as initial inputs.

27

Figure 11: The optimized imidazole molecule and ion was imported into COSMO

Figure 12: Prediction of basicity using COSMO-RS

28

Figure 13: Prediction of pKa value of amine solution in water at temperature of 25oC

29

3.4. Gantt Chart and Key Milestone (FYP II January Semester)

Table 1: Gantt Chart for the FYPII

N

o Activties / Tasks 1 2 3 4 5 6 7 8 9

1

0

1

1

1

2

1

3

1

4

1

5

1 Project Work Continue

Identification of amine

candidates

Geometry optimization

of amine

Basicity prediction using

COSMO

2 Progress Report

3 Pre Sedex

4 Submission of Draft

5 Submission

Softbound

Technical Paper

6 Oral Presentation

7 Submission of

Hardbound

30

CHAPTER 4

RESULT AND DISCUSSION

4. RESULT AND DISSUSION

4.1. Correlation between experimental and calculated pKa

To validate the result, the predicted basicity of amines were compared with

experimental results reported in literature. For comparison, 46 experimental data

points over 24 to 27 degree Celsius were used. The experimental data is based on

the literature.

Table 2: Experimental and Predicted pKa data using COSMO-RS for 46 amines

molecule

Amine Experimental

pKa value

References Predicted

pKa value

1 2-(dimethylamino)ethanol 9.23 a 8.9082 Alkanolamine

2 2-(diisopropylamino)ethanol 9.97 a 8.7145

3 methyldiethanolamine 8.52 a 6.4864

4 triethanolamine 7.76 a 4.9620

5 1-diethylamino-2-propanol 10.18 c 7.8509

6 2-(dimethylamino)-2-methyl-1-propanol 10.34 c 8.6800

7 3-(dimethylamino)-1,2-propanediol 9.14 c 7.3381

8 3-diethylamino-1,2-propanediol 9.89 c 8.8513

9 3-diethylamino-1-propanol 10.29 c 9.1650

10 4-diethylamino-2-butanol 9.94 d 9.6742

11 4-ethyl-methyl-amino-2-butanol 9.82 c 7.5262

12 N-isopropyldiethanolamine 9.12 c 7.8804

31

13 N-tert-butyldiethanolamine 9.06 c 7.1598

14 2-diethylaminoethanol 10.01 c 8.5663

15 1-dimethylamino-2-propanol 9.76 c 10.5978

16 3-dimethylamino-2,2-dimethyl-1-propanol 9.54 c 8.0679

17 N-ethyldiethanolamine 8.86 c 6.6199

18 3-dimethylamino-1-propanol 9.54 c 9.4595

19 Triethylamine 10.78 e 8.8846 Alkylamine

20 Trimethylamine 9.80 a 9.8503

21 1-Dipropylaminopropane 10.26 f 8.1489

22 4-ethylmorpholine 7.71 b 6.3038 Cyclic Amine

23 2-morpholinoethanol 6.93 b 7.8087

24 1,4-bis(2-hydroxyethyl)piperazine 7.70 b 5.6807

25 3-morpholino-1,2-propanediol 6.76 b 5.3589

26 1-methyl-2-piperidineethanol 9.89 c 8.8247

27 3-hydroxy-1-methylpiperidine 8.49 c 8.2620

28 1-(2-hydroxyethyl)pyrrolidine 9.86 c 8.8945

29 3-pyrrolidino-1,2-propanediol 9.64 c 8.6909

30 1-(2-hydroxyethyl)piperidine 9.76 c 7.9745

31 1-ethyl-3-hydroxypiperidine 9.21 c 8.2004

32 3-piperidino-1,2-propanediol 9.49 c 8.2366

33 1-n-Butylpiperidine 10.47 f 9.7208

34 1,2-Dimethylpiperidine 10.26 f 9.6359

35 1,2-Dimethylpyrrolidine 10.26 f 10.4037

36 1-Ethyl-2-methylpiperidine 10.70 f 10.0471

37 1-Ethyl-2-methylpyrrolidine 10.64 f 10.2597

38 N-Ethylpiperidine 10.40 f 9.7282

39 N-Methylpyrrolidine 10.46 f 9.8118

40 N-Methyltrimethyleneimine 10.40 f 9.9339

41 1-n-Propylpiperidine 10.48 f 9.9376

32

42 1-Methyl-2-n-butyl-pyrrolidine 10.24 f 9.6891

43 N-Methylpiperidine 10.08 f 9.8125

44 imidazole 6.99 b 6.9728 Aromatic

Amine 45 4-methylimidazole 7.54 b 7.8610

46 1,2-dimethylimidazole 8.00 b 7.9773

a Data from Perrin [32]. b Data from Hedetaka [20]. c Data from Firoz [33].d

The experimental pKa values that listed in the Table 2 above, and the values

predicted by the COSMO-RS//B3P/TZVP method are plotted in the Figure 14.

Figure 14: Graph of pKa values calculated using COSMO-RS//BP/TZVP versus

Experimental value

From the correlation above, pKa values obtained with the COSMO-

RS//BP/TZVP method showed a good correlation with experimental values. It can be

notice that a plot of experimental pKa value versus predicted value using COSMO-RS

gives a linear trend with R2 of 0.6. Therefore, COSMO-RS software can be used with

y = 0.9735x - 0.7227R² = 0.6033

4

5

6

7

8

9

10

11

12

4 5 6 7 8 9 10 11 12

Pre

dic

ted

pK

a V

alu

e

Experimental pKa Value

Predicted vs Experimental pKa value

33

reasonable confidence to estimate the pKa values of tertiary amines of different

structure. The regression slopes is 0.97 which is also consistent with the theoretical

value of 1. This is considered to be a consequence of the COSMO-RS model that

successfully treats hydrogen bonding with a simple description.

𝑟𝑚𝑠𝑑 = √(1

𝑁) ∑(𝑒𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 − 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑)2

𝑁

1

The root-mean-square deviation (rmsd) of basicity of the amine solution inside

water is 1.2 pKa-units. This agrees with the expected deviation from the work done by

Eckert & Klamt [31] which is around 1.0 pKa-units.

Figure 15: Distribution of pKa value for different amine types

Figure 15 shows the distribution of experimental versus predicted pKa value by

COSMO-RS//BP/TZVP based on different tertiary amine structure. As can be seen

from the distribution, most of the predicted value lies on the bottom of the theoretical

(x=y) line which indicates that the overall predicted value was lower than it actual

4

5

6

7

8

9

10

11

12

4 6 8 10 12

Pre

dic

ted

pK

a V

alu

e

Experimental pKa Value

Predicted vs Experimental pKa value

Alkanolamine

Alkylamine

Cyclic Amine

Aromatic

Linear (Theoritical Line)

34

value. This can be explain based on the work described in the literature by Eckert and

Klamt [31] as they demonstrated the pKa prediction for organic bases and they found

that tertiary aliphatic amine (i.e. alkanolamine, alkylamine, cyclic amine) require a

systematic correction of 2 pKa units because of problem faced by most of the quantum

chemical continuum solvation models. This correction factor have not been adopted in

this work. As for aromatic amine, it shows a good correlation with the experimental

value with rmsd of 0.19.

Using COSMO-RS//BP/TZVP method to determine the thermodynamic properties of

amine in H2O system gives us advantage because of it has low computational cost due

to RI approximation. Thus, this method is suitable for screening purposes.

35

4.2. Correlation between predicted basicity value with absorption rate and

absorption amount.

After benchmarking, the absorption rate and absorption amount of the amine were

examine to study the effect of basicity on the performance of amine. The pKa value is

an important fundamental property which affects the kinetics and possibly the

mechanism of the capture process [34]. Many previous studies also reported on a

Brønsted relationship between the rate constant of the reaction of amines with CO2

and the basicity of such amines [35]. In tertiary amines, the rate shows a strong

dependence on pKa because of the base-catalyzed mechanism. Puxty et al.[18] studied

a relation between CO2 absorption rates and calculated pKa values among 76 amines

and found that the larger is the value of pKa, the higher is the absorption rate, as a

general trend. This trend was confirmed in Figure 18, where the absorption rates were

plotted against the predicted pKa values. However, based on its small R2 value, Figure

16 indicates that the absorption rate is also governed by other factors such as steric

hindrance around the amino moiety. Based on the Figure 17, it can also be generalize

that the larger is the value of pKa, the higher is the absorption amount.

Table 3: Predicted pKa value from this work is to be compare with the absorption

rate and absorption amount

Amine

Predicted

pKa value

Absorption

Rate (g-

CO2/L-

soln/min)

Absorption

Amount (g-

CO2/L-soln)

Referenc

es

1 2-(dimethylamino)ethanol 8.9082 1.70 73 c

2 2-(diisopropylamino)ethanol 8.7145 1.01 57 c

3 methyldiethanolamine 6.4864 1.56 55 c

4 triethanolamine 4.9620 0.75 22 c

5 1-diethylamino-2-propanol 7.8509 1.66 78 c

6 2-(dimethylamino)-2-methyl-1-propanol 8.6800 1.18 88 c

7 3-(dimethylamino)-1,2-propanediol 7.3381 1.28 70 c

8 3-diethylamino-1,2-propanediol 8.8513 3.40 73 c

9 3-diethylamino-1-propanol 9.1650 2.60 89 c

36

10 4-ethyl-methyl-amino-2-butanol 7.5262 1.37 69 c

11 N-isopropyldiethanolamine 7.8804 1.36 58 c

12 N-tert-butyldiethanolamine 7.1598 1.91 52 c

13 2-diethylaminoethanol 8.5663 2.49 94 c

14 1-dimethylamino-2-propanol 10.5978 2.24 92 c

15 3-dimethylamino-2,2-dimethyl-1-propanol 8.0679 1.08 57 c

16 N-ethyldiethanolamine 6.6199 0.70 39 c

17 3-dimethylamino-1-propanol 9.4595 1.47 71 c

18 1-methyl-2-piperidineethanol 8.8247 3.17 77 c

19 3-hydroxy-1-methylpiperidine 8.2620 1.08 55 c

20 1-(2-hydroxyethyl)pyrrolidine 8.8945 2.41 94 c

21 3-pyrrolidino-1,2-propanediol 8.6909 1.25 47 c

22 1-(2-hydroxyethyl)piperidine 7.9745 2.22 83 c

23 1-ethyl-3-hydroxypiperidine 8.2004 0.96 56 c

24 3-piperidino-1,2-propanediol 8.2366 3.33 57 c

34 4-(Dimethylamino)-1-butanol 9.2296 3.58 97 c

35 6-dimethylamino-1-hexanol 9.5948 3.97 80 c

c CO2 absorption rates were calculated at 50% of the 60 min CO2 loading and 40 °C. CO2 absorption amount

at 60 min CO2 loading at 40 °C [33].

37

Figure 16: Predicted pKa value vs Absorption Rate

Figure 17: Predicted pKa value vs Absorption Amount

y = 0.4329x - 1.6624R² = 0.2705

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

0.0000 2.0000 4.0000 6.0000 8.0000 10.0000 12.0000

Ab

sorp

tio

n R

ate

(g-C

O2

/L-s

oln

/min

)

Predicted pKa Value (COSMO-RS)

Predicted pKa value vs Absorption Rate

y = 12.02x - 30.703R² = 0.5302

0

20

40

60

80

100

120

0.0000 2.0000 4.0000 6.0000 8.0000 10.0000 12.0000

Ab

sorp

tio

n A

mo

un

t (g

-CO

2/L

-so

ln)

Predicted pKa Value (COSMO-RS)

Predicted pKa value vs Absorption Amount

38

By having this two generalization which relate the predicted pKa value from COSMO-

RS with absorption rate and absorption amount, we can roughly predict and quickly

screen the performance of tertiary amine (in terms of absorption rate and capacity) by

just having to simulate and predicted pKa value for that particular molecule. Of course

this screening technique was not perfectly accurate but at some point it can be a reliable

screening technique to help reduce time and cost for screening unwanted amine

candidate in the lab scale.

In this work, we have predicted the absorption rate and absorption capacity for 26

amines molecule by using this technique. Based on the equation of regression line

obtained in the Figure 18 and Figure 19, we estimated the absorption rate and amount

by using the equation below.

𝑦 = 0.4329𝑥 − 1.6624 → 𝑓𝑜𝑟 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒

𝑦 = 12.02𝑥 − 30.703 → 𝑓𝑜𝑟 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛 𝑎𝑚𝑜𝑢𝑛𝑡

Table4: Prediction of absorption rate and absorption capacity

No Name

Predicted

pKa

value

Absorption

Rate (g-

CO2/L-

soln/min)

Absorption

Amount (g-

CO2/L-

soln)

1 1,2-Dimethylpyrrolidine 10.4037 2.84 94.349474

2 1-Ethyl-2-methylpyrrolidine 10.2597 2.78 92.6182334

3 1-Ethyl-2-methylpiperidine 10.0471 2.69 90.0630218

4 1-n-Propylpiperidine 9.9376 2.64 88.7471924

5 N-Methyltrimethyleneimine 9.9339 2.64 88.7028386

6 Trimethylamine 9.8503 2.60 87.6971252

7 N-Methylpiperidine 9.8125 2.59 87.24325

8 N-Methylpyrrolidine 9.8118 2.59 87.2344754

9 N-Ethylpiperidine 9.7282 2.55 86.2302044

10 1-n-Butylpiperidine 9.7208 2.55 86.140415

11 1-Methyl-2-n-butyl-pyrrolidine 9.6891 2.53 85.7597416

12 1,2-Dimethylpiperidine 9.6359 2.51 85.119917

39

13 Triethylamine 8.8846 2.18 76.0897718

14 1-Dipropylaminopropane 8.1489 1.87 67.2468982

15 1,2-dimethylimidazole 7.9773 1.79 65.18406222

16 4-methylimidazole 7.8610 1.74 63.78664647

17 2-morpholinoethanol 7.8087 1.72 63.15797655

18 imidazole 6.9728 1.36 53.11006538

19 4-ethylmorpholine 6.3038 1.07 45.06810938

20 1,4-bis(2-hydroxyethyl)piperazine 5.6807 0.80 37.57864258

21 3-morpholino-1,2-propanediol 5.3589 0.66 33.7104228

Based on Table 4, the five most outstanding amine candidates in terms of their

absorption rate and absorption capacity are 1,2-Dimethylpyrrolidine, 1-Ethyl-2-

methylpyrrolidine, 1-Ethyl-2-methylpiperidine, 1-n-Propylpiperidine, and N-

Methyltrimethyleneimine. This estimation might not accurate, but at least it gives us

insight that we might not want to go and spend our time to run a performance testing

experiment on 3-morpholino-1,2-propanediol.

40

CHAPTER 5

CONCLUSION AND RECOMENDATION

5. CONCLUSION & RECOMENDATION

The basicity of amine based solvent were by the DFT-based COSMO-RS method.

The agreement between the predicted and experimental result from literature indicates

that COSMO-RS method is applicable to be used in this system as a tool for initial

screening. It is expected that this work will be able to provide initial screening tool to

systematically screen possible amine candidates for CO2 capture using COSMO-RS.

Using the model developed in this study, the performance of amine in terms of

absorption rate and absorption capacity can be evaluated. The importance of this work

is, it should be able to reduce time and cost in selecting appropriate amine especially

from huge number of candidates. It should also increase the opportunity to select new

amine candidates.

To further improve this work, there are some recommendation that can be

implement in the future:

This work is only valid for tertiary amine based solvent and may not be

reliable to the other types of amine. Thus, for further improvement, the

relationship between other types of amine with its respective

thermodynamic properties should be study.

Other than absorption amount and absorption rate, heat of reaction also one

of the important parameter in evaluating the performance of amine because

most of the cost in operating the amine based PCC comes from absorbent

regeneration process. Thus, it is recommended to include also the

assessment for heat of reaction in further study.

41

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